Overview

Dataset statistics

Number of variables12
Number of observations3172
Missing cells797
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory322.2 KiB
Average record size in memory104.0 B

Variable types

Categorical3
Numeric9

Alerts

PropertyGFABuilding(s) is highly overall correlated with SiteEnergyUse(kBtu)High correlation
SiteEnergyUse(kBtu) is highly overall correlated with PropertyGFABuilding(s)High correlation
Use_Steam is highly imbalanced (77.4%)Imbalance
ENERGYSTARScore has 797 (25.1%) missing valuesMissing
SiteEnergyUse(kBtu) has unique valuesUnique
NumberofBuildings has 90 (2.8%) zerosZeros
PropertyGFAParking has 2694 (84.9%) zerosZeros

Reproduction

Analysis started2025-12-15 14:14:24.450394
Analysis finished2025-12-15 14:14:34.316432
Duration9.87 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Distinct23
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size49.6 KiB
Low-Rise Multifamily
952 
Mid-Rise Multifamily
553 
Small- and Mid-Sized Office
286 
Other
244 
Warehouse
184 
Other values (18)
953 

Length

Max length27
Median length22
Mean length17.430013
Min length5

Characters and Unicode

Total characters55288
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowOther

Common Values

ValueCountFrequency (%)
Low-Rise Multifamily952
30.0%
Mid-Rise Multifamily553
17.4%
Small- and Mid-Sized Office286
 
9.0%
Other244
 
7.7%
Warehouse184
 
5.8%
Large Office156
 
4.9%
Mixed Use Property128
 
4.0%
High-Rise Multifamily103
 
3.2%
Retail Store85
 
2.7%
Hotel74
 
2.3%
Other values (13)407
12.8%

Length

2025-12-15T15:14:34.402935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
multifamily1608
24.3%
low-rise952
14.4%
mid-rise553
 
8.4%
office480
 
7.3%
and286
 
4.3%
small286
 
4.3%
mid-sized286
 
4.3%
other244
 
3.7%
warehouse196
 
3.0%
large156
 
2.4%
Other values (28)1569
23.7%

Most occurring characters

ValueCountFrequency (%)
i7379
 
13.3%
e4382
 
7.9%
l4211
 
7.6%
3444
 
6.2%
a2948
 
5.3%
t2706
 
4.9%
M2613
 
4.7%
f2608
 
4.7%
-2258
 
4.1%
s2117
 
3.8%
Other values (33)20622
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)55288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i7379
 
13.3%
e4382
 
7.9%
l4211
 
7.6%
3444
 
6.2%
a2948
 
5.3%
t2706
 
4.9%
M2613
 
4.7%
f2608
 
4.7%
-2258
 
4.1%
s2117
 
3.8%
Other values (33)20622
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)55288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i7379
 
13.3%
e4382
 
7.9%
l4211
 
7.6%
3444
 
6.2%
a2948
 
5.3%
t2706
 
4.9%
M2613
 
4.7%
f2608
 
4.7%
-2258
 
4.1%
s2117
 
3.8%
Other values (33)20622
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)55288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i7379
 
13.3%
e4382
 
7.9%
l4211
 
7.6%
3444
 
6.2%
a2948
 
5.3%
t2706
 
4.9%
M2613
 
4.7%
f2608
 
4.7%
-2258
 
4.1%
s2117
 
3.8%
Other values (33)20622
37.3%

Latitude
Real number (ℝ)

Distinct2719
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.624785
Minimum47.50224
Maximum47.73387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2025-12-15T15:14:34.530431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum47.50224
5-th percentile47.54393
Q147.600962
median47.61918
Q347.657157
95-th percentile47.713065
Maximum47.73387
Range0.23163
Interquartile range (IQR)0.056195

Descriptive statistics

Standard deviation0.047117487
Coefficient of variation (CV)0.00098934803
Kurtosis-0.10822197
Mean47.624785
Median Absolute Deviation (MAD)0.02647
Skewness0.1601006
Sum151065.82
Variance0.0022200576
MonotonicityNot monotonic
2025-12-15T15:14:34.674995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.662469
 
0.3%
47.615987
 
0.2%
47.622086
 
0.2%
47.525495
 
0.2%
47.615435
 
0.2%
47.623955
 
0.2%
47.600714
 
0.1%
47.522544
 
0.1%
47.62394
 
0.1%
47.599384
 
0.1%
Other values (2709)3119
98.3%
ValueCountFrequency (%)
47.502241
< 0.1%
47.509591
< 0.1%
47.510181
< 0.1%
47.510421
< 0.1%
47.510981
< 0.1%
47.511041
< 0.1%
47.511272
0.1%
47.511681
< 0.1%
47.511691
< 0.1%
47.513041
< 0.1%
ValueCountFrequency (%)
47.733871
< 0.1%
47.733751
< 0.1%
47.733681
< 0.1%
47.73361
< 0.1%
47.733571
< 0.1%
47.733511
< 0.1%
47.733311
< 0.1%
47.733161
< 0.1%
47.733151
< 0.1%
47.732791
< 0.1%

Longitude
Real number (ℝ)

Distinct2511
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.33521
Minimum-122.41425
Maximum-122.26028
Zeros0
Zeros (%)0.0%
Negative3172
Negative (%)100.0%
Memory size49.6 KiB
2025-12-15T15:14:34.811016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-122.41425
5-th percentile-122.38643
Q1-122.35076
median-122.33264
Q3-122.32022
95-th percentile-122.29181
Maximum-122.26028
Range0.15397
Interquartile range (IQR)0.0305325

Descriptive statistics

Standard deviation0.026645138
Coefficient of variation (CV)-0.00021780433
Kurtosis0.24891371
Mean-122.33521
Median Absolute Deviation (MAD)0.014895
Skewness-0.17813393
Sum-388047.27
Variance0.00070996337
MonotonicityNot monotonic
2025-12-15T15:14:34.963684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.298988
 
0.3%
-122.353987
 
0.2%
-122.333696
 
0.2%
-122.324686
 
0.2%
-122.317695
 
0.2%
-122.333795
 
0.2%
-122.324175
 
0.2%
-122.330645
 
0.2%
-122.325925
 
0.2%
-122.328114
 
0.1%
Other values (2501)3116
98.2%
ValueCountFrequency (%)
-122.414251
< 0.1%
-122.411821
< 0.1%
-122.411781
< 0.1%
-122.411691
< 0.1%
-122.410371
< 0.1%
-122.410361
< 0.1%
-122.410311
< 0.1%
-122.409761
< 0.1%
-122.409741
< 0.1%
-122.409011
< 0.1%
ValueCountFrequency (%)
-122.260281
< 0.1%
-122.260341
< 0.1%
-122.261662
0.1%
-122.261721
< 0.1%
-122.261771
< 0.1%
-122.26181
< 0.1%
-122.262161
< 0.1%
-122.262231
< 0.1%
-122.262351
< 0.1%
-122.262771
< 0.1%

YearBuilt
Real number (ℝ)

Distinct113
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.6337
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2025-12-15T15:14:35.112198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1908
Q11948
median1975
Q31997
95-th percentile2012
Maximum2015
Range115
Interquartile range (IQR)49

Descriptive statistics

Standard deviation33.219065
Coefficient of variation (CV)0.016874173
Kurtosis-0.88331443
Mean1968.6337
Median Absolute Deviation (MAD)24
Skewness-0.53754265
Sum6244506
Variance1103.5063
MonotonicityNot monotonic
2025-12-15T15:14:35.258392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201467
 
2.1%
200066
 
2.1%
196862
 
2.0%
200860
 
1.9%
198859
 
1.9%
198958
 
1.8%
199957
 
1.8%
197055
 
1.7%
200154
 
1.7%
200254
 
1.7%
Other values (103)2580
81.3%
ValueCountFrequency (%)
190051
1.6%
19017
 
0.2%
190211
 
0.3%
19033
 
0.1%
190414
 
0.4%
19059
 
0.3%
190618
 
0.6%
190731
1.0%
190826
0.8%
190929
0.9%
ValueCountFrequency (%)
201535
1.1%
201467
2.1%
201350
1.6%
201235
1.1%
201115
 
0.5%
201023
 
0.7%
200939
1.2%
200860
1.9%
200741
1.3%
200644
1.4%

NumberofBuildings
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0655738
Minimum0
Maximum27
Zeros90
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2025-12-15T15:14:35.373747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum27
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.89681462
Coefficient of variation (CV)0.84162603
Kurtosis387.8697
Mean1.0655738
Median Absolute Deviation (MAD)0
Skewness16.922782
Sum3380
Variance0.80427646
MonotonicityNot monotonic
2025-12-15T15:14:35.474880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
12990
94.3%
090
 
2.8%
236
 
1.1%
321
 
0.7%
412
 
0.4%
59
 
0.3%
63
 
0.1%
83
 
0.1%
102
 
0.1%
111
 
< 0.1%
Other values (5)5
 
0.2%
ValueCountFrequency (%)
090
 
2.8%
12990
94.3%
236
 
1.1%
321
 
0.7%
412
 
0.4%
59
 
0.3%
63
 
0.1%
83
 
0.1%
91
 
< 0.1%
102
 
0.1%
ValueCountFrequency (%)
271
 
< 0.1%
231
 
< 0.1%
161
 
< 0.1%
141
 
< 0.1%
111
 
< 0.1%
102
 
0.1%
91
 
< 0.1%
83
 
0.1%
63
 
0.1%
59
0.3%

NumberofFloors
Real number (ℝ)

Distinct43
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6270492
Minimum0
Maximum99
Zeros14
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2025-12-15T15:14:35.933017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile11.45
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.8664213
Coefficient of variation (CV)1.0517332
Kurtosis62.009403
Mean4.6270492
Median Absolute Deviation (MAD)1
Skewness5.7213781
Sum14677
Variance23.682056
MonotonicityNot monotonic
2025-12-15T15:14:36.067213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4666
21.0%
3648
20.4%
1424
13.4%
2394
12.4%
6294
9.3%
5289
9.1%
7144
 
4.5%
859
 
1.9%
1132
 
1.0%
1031
 
1.0%
Other values (33)191
 
6.0%
ValueCountFrequency (%)
014
 
0.4%
1424
13.4%
2394
12.4%
3648
20.4%
4666
21.0%
5289
9.1%
6294
9.3%
7144
 
4.5%
859
 
1.9%
918
 
0.6%
ValueCountFrequency (%)
991
 
< 0.1%
491
 
< 0.1%
423
0.1%
412
0.1%
401
 
< 0.1%
391
 
< 0.1%
381
 
< 0.1%
372
0.1%
362
0.1%
341
 
< 0.1%

PropertyGFAParking
Real number (ℝ)

Zeros 

Distinct470
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7495.4092
Minimum0
Maximum407795
Zeros2694
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2025-12-15T15:14:36.204031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile44745.1
Maximum407795
Range407795
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28997.958
Coefficient of variation (CV)3.8687625
Kurtosis47.473691
Mean7495.4092
Median Absolute Deviation (MAD)0
Skewness6.0498259
Sum23775438
Variance8.4088156 × 108
MonotonicityNot monotonic
2025-12-15T15:14:36.354536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02694
84.9%
133203
 
0.1%
220002
 
0.1%
300002
 
0.1%
108002
 
0.1%
258002
 
0.1%
129602
 
0.1%
1001762
 
0.1%
204162
 
0.1%
76001
 
< 0.1%
Other values (460)460
 
14.5%
ValueCountFrequency (%)
02694
84.9%
381
 
< 0.1%
2601
 
< 0.1%
4151
 
< 0.1%
6041
 
< 0.1%
7561
 
< 0.1%
8001
 
< 0.1%
9191
 
< 0.1%
12631
 
< 0.1%
13921
 
< 0.1%
ValueCountFrequency (%)
4077951
< 0.1%
3689801
< 0.1%
3351091
< 0.1%
3037071
< 0.1%
2856881
< 0.1%
2729001
< 0.1%
2392521
< 0.1%
2286681
< 0.1%
2065971
< 0.1%
2065801
< 0.1%

PropertyGFABuilding(s)
Real number (ℝ)

High correlation 

Distinct3000
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76939.364
Minimum3636
Maximum1172127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2025-12-15T15:14:36.499854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3636
5-th percentile21012.15
Q127485.25
median42295.5
Q382015.25
95-th percentile261089.5
Maximum1172127
Range1168491
Interquartile range (IQR)54530

Descriptive statistics

Standard deviation99287.823
Coefficient of variation (CV)1.2904685
Kurtosis26.47592
Mean76939.364
Median Absolute Deviation (MAD)18149
Skewness4.3020431
Sum2.4405166 × 108
Variance9.8580717 × 109
MonotonicityNot monotonic
2025-12-15T15:14:36.642027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360009
 
0.3%
259208
 
0.3%
288007
 
0.2%
216007
 
0.2%
240006
 
0.2%
307204
 
0.1%
302404
 
0.1%
223204
 
0.1%
450003
 
0.1%
252003
 
0.1%
Other values (2990)3117
98.3%
ValueCountFrequency (%)
36361
< 0.1%
109251
< 0.1%
112851
< 0.1%
114401
< 0.1%
116851
< 0.1%
119681
< 0.1%
127691
< 0.1%
128061
< 0.1%
131571
< 0.1%
141011
< 0.1%
ValueCountFrequency (%)
11721271
< 0.1%
10479341
< 0.1%
10048131
< 0.1%
9706471
< 0.1%
9624281
< 0.1%
9342921
< 0.1%
8880491
< 0.1%
8617021
< 0.1%
7945921
< 0.1%
7913961
< 0.1%

ENERGYSTARScore
Real number (ℝ)

Missing 

Distinct100
Distinct (%)4.2%
Missing797
Missing (%)25.1%
Infinite0
Infinite (%)0.0%
Mean67.231579
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2025-12-15T15:14:36.783274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q152
median74
Q389
95-th percentile99
Maximum100
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.941015
Coefficient of variation (CV)0.40071965
Kurtosis-0.29888023
Mean67.231579
Median Absolute Deviation (MAD)18
Skewness-0.81724458
Sum159675
Variance725.81829
MonotonicityNot monotonic
2025-12-15T15:14:36.928462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10087
 
2.7%
9870
 
2.2%
9662
 
2.0%
8952
 
1.6%
9351
 
1.6%
9549
 
1.5%
9147
 
1.5%
9947
 
1.5%
9246
 
1.5%
8146
 
1.5%
Other values (90)1818
57.3%
(Missing)797
25.1%
ValueCountFrequency (%)
133
1.0%
210
 
0.3%
313
 
0.4%
45
 
0.2%
58
 
0.3%
68
 
0.3%
710
 
0.3%
89
 
0.3%
95
 
0.2%
109
 
0.3%
ValueCountFrequency (%)
10087
2.7%
9947
1.5%
9870
2.2%
9743
1.4%
9662
2.0%
9549
1.5%
9444
1.4%
9351
1.6%
9246
1.5%
9147
1.5%

SiteEnergyUse(kBtu)
Real number (ℝ)

High correlation  Unique 

Distinct3172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4189992.5
Minimum57133.199
Maximum98960776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2025-12-15T15:14:37.069011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum57133.199
5-th percentile520911.44
Q1934115.56
median1787633.5
Q34167798.2
95-th percentile16279284
Maximum98960776
Range98903643
Interquartile range (IQR)3233682.6

Descriptive statistics

Standard deviation7132304.3
Coefficient of variation (CV)1.7022236
Kurtosis34.42321
Mean4189992.5
Median Absolute Deviation (MAD)1047125.3
Skewness4.8456996
Sum1.3290656 × 1010
Variance5.0869764 × 1013
MonotonicityNot monotonic
2025-12-15T15:14:37.212305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7226362.51
 
< 0.1%
83879331
 
< 0.1%
67945841
 
< 0.1%
141726061
 
< 0.1%
120866161
 
< 0.1%
57587951
 
< 0.1%
6298131.51
 
< 0.1%
137238201
 
< 0.1%
45737771
 
< 0.1%
160166441
 
< 0.1%
Other values (3162)3162
99.7%
ValueCountFrequency (%)
57133.199221
< 0.1%
79711.796881
< 0.1%
90558.703131
< 0.1%
97690.398441
< 0.1%
1069181
< 0.1%
111969.70311
< 0.1%
1131301
< 0.1%
116486.60161
< 0.1%
117438.39841
< 0.1%
123767.20311
< 0.1%
ValueCountFrequency (%)
989607761
< 0.1%
906096401
< 0.1%
680907281
< 0.1%
653369801
< 0.1%
650472841
< 0.1%
591076201
< 0.1%
587613041
< 0.1%
577644081
< 0.1%
564852041
< 0.1%
531661561
< 0.1%

Use_Steam
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size49.6 KiB
0
3056 
1
 
116

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3172
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03056
96.3%
1116
 
3.7%

Length

2025-12-15T15:14:37.337267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T15:14:37.417289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03056
96.3%
1116
 
3.7%

Most occurring characters

ValueCountFrequency (%)
03056
96.3%
1116
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03056
96.3%
1116
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03056
96.3%
1116
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03056
96.3%
1116
 
3.7%

Use_Gas
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size49.6 KiB
1
1983 
0
1189 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3172
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11983
62.5%
01189
37.5%

Length

2025-12-15T15:14:37.506163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T15:14:37.578262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11983
62.5%
01189
37.5%

Most occurring characters

ValueCountFrequency (%)
11983
62.5%
01189
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11983
62.5%
01189
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11983
62.5%
01189
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11983
62.5%
01189
37.5%

Interactions

2025-12-15T15:14:33.083160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:24.966430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:25.879838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:26.926133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:27.888112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:29.144358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:30.083694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:31.162093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:32.148198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:33.186128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:25.062784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:25.991224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:27.027125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:27.984684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:29.242905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:30.194089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:31.262756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:32.249103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:33.300972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:25.173239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:26.114724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:27.142612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:28.097451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:29.355638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:30.320035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:31.383621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:32.363180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:33.404182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:25.275505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:26.231955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:27.244440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:28.200541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:29.461528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:30.432302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:31.492861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:32.464882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:33.505983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:25.378493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:26.342474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:27.349437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:28.293540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:29.559670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:30.592354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:31.597038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:32.574663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:33.605775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:25.474061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:26.452706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:27.448035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:28.392197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:29.658114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:30.710047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:31.703369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:32.671610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:33.720648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:25.580667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:26.576643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:27.561814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:28.503602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:29.770196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:30.828593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:31.821417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:32.780740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:33.831367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:25.684316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:26.698306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:27.670975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:28.932772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:29.878628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:30.944312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:31.934793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:32.881723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:33.933938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:25.781379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:26.810858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:27.775948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:29.039740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:29.978941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:31.051558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:32.039148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T15:14:32.979998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-15T15:14:37.642333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ENERGYSTARScoreLatitudeLongitudeNumberofBuildingsNumberofFloorsPrimaryPropertyTypePropertyGFABuilding(s)PropertyGFAParkingSiteEnergyUse(kBtu)Use_GasUse_SteamYearBuilt
ENERGYSTARScore1.0000.092-0.0400.0520.1570.1180.0810.017-0.1850.1090.0000.082
Latitude0.0921.000-0.0140.0580.0600.216-0.0560.019-0.0880.1580.2790.150
Longitude-0.040-0.0141.0000.026-0.1060.149-0.024-0.0500.0170.0870.178-0.050
NumberofBuildings0.0520.0580.0261.000-0.0270.1320.0550.0060.0420.0290.0000.038
NumberofFloors0.1570.060-0.106-0.0271.0000.3490.4530.2500.2860.0380.2690.301
PrimaryPropertyType0.1180.2160.1490.1320.3491.0000.1800.1570.2830.3450.2690.189
PropertyGFABuilding(s)0.081-0.056-0.0240.0550.4530.1801.0000.2270.7410.1160.1940.287
PropertyGFAParking0.0170.019-0.0500.0060.2500.1570.2271.0000.3080.0000.0000.240
SiteEnergyUse(kBtu)-0.185-0.0880.0170.0420.2860.2830.7410.3081.0000.1220.2290.160
Use_Gas0.1090.1580.0870.0290.0380.3450.1160.0000.1221.0000.0210.345
Use_Steam0.0000.2790.1780.0000.2690.2690.1940.0000.2290.0211.0000.168
YearBuilt0.0820.150-0.0500.0380.3010.1890.2870.2400.1600.3450.1681.000

Missing values

2025-12-15T15:14:34.089721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-15T15:14:34.228492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PrimaryPropertyTypeLatitudeLongitudeYearBuiltNumberofBuildingsNumberofFloorsPropertyGFAParkingPropertyGFABuilding(s)ENERGYSTARScoreSiteEnergyUse(kBtu)Use_SteamUse_Gas
0Hotel47.61220-122.3379919271.01208843460.07226362.511
1Hotel47.61317-122.3339319961.011150648850261.08387933.001
3Hotel47.61412-122.3366419261.01006132056.06794584.011
4Hotel47.61375-122.3404719801.0186200011358075.014172606.001
5Other47.61623-122.3365719991.023719860090NaN12086616.001
6Hotel47.61390-122.3328319261.01108300827.05758795.001
7Other47.61327-122.3313619261.080102761NaN6298131.511
8Hotel47.60294-122.3326319041.015016398443.013723820.001
9Mid-Rise Multifamily47.60284-122.3318419101.061496622161.04573777.011
10Hotel47.60695-122.3341419691.0111927913388430.016016644.011
PrimaryPropertyTypeLatitudeLongitudeYearBuiltNumberofBuildingsNumberofFloorsPropertyGFAParkingPropertyGFABuilding(s)ENERGYSTARScoreSiteEnergyUse(kBtu)Use_SteamUse_Gas
3363Other47.72126-122.2973519491.01011285NaN6.456654e+0501
3364Other47.67295-122.3922819111.01016795NaN9.366165e+0501
3365Other47.67734-122.3762419721.01012769NaN5.117308e+0601
3367Other47.63228-122.3157419121.01023445NaN5.976246e+0601
3368Mixed Use Property47.60775-122.3022519941.01020050NaN1.813404e+0601
3370Other47.54067-122.3744119821.01018261NaN9.320821e+0501
3372Other47.59625-122.3228320041.01016000NaN9.502762e+0501
3373Other47.63644-122.3578419741.01013157NaN5.765898e+0601
3374Mixed Use Property47.52832-122.3243119891.01014101NaN7.194712e+0501
3375Mixed Use Property47.53939-122.2953619381.01018258NaN1.152896e+0601